shuttle-2.5-miniShuttleai
Start Chat
12B Params FP8 Open Weights Inference Available

Shuttle-2.5-mini is a 13 billion parameter multilingual language model developed by ShuttleAI Inc., fine-tuned from Mistral-Nemo-Base-2407. It is specifically designed to excel in complex chat, reasoning, and agent tasks, with a unique focus on emulating the writing style of Claude 3 models and extensive training on role-playing data. This model supports a 128k context window and is pretrained on a large proportion of multilingual and code data, making it suitable for diverse communication and specialized interactive applications.

Loading preview...

Parameters:12BContext length:32kArchitecture:TransformerPrecision:FP8Quantized variants:AvailableLast updated:July 2024
0.0M
0.0K

Model tree for

shuttleai/shuttle-2.5-mini
Finetunes 5 models Quantizations 8 models
Popular Sampler Settings

Most commonly used values from Featherless users

temperature

This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.

–

top_p

This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.

–

top_k

This limits the number of top tokens to consider. Set to -1 to consider all tokens.

–

frequency_penalty

This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.

–

presence_penalty

This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.

0.5

repetition_penalty

This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.

1.1

min_p

This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.

0.05